One issue with data track mining of large databases of geo-referenced trajectories is that algorithms that cluster data tracks may require time-consuming and inefficient user interaction. For example, user interaction may be required to determine how many clusters there are within the track data, and user interaction may be required to select a preferred or “best” cluster from the clusters. Further, clustering algorithms may apply metrics that do not satisfy requirements, or axioms, of a metric space. Therefore, artifacts may appear when conventional clustering algorithms are used.
Thus, there are general needs for algorithms to determine a number of clusters in track data with little or no user input. There are also general needs to determine these clusters based on a metric space that satisfies axioms of a metric space so that artifacts may be reduced or eliminated.